By formulating LIDAR localization as a recursive Bayesian inference problem, [1]} embeds both LIDAR intensity maps and online point cloud sweeps in a sharing space for fully differentiable pose estimation. Instead of operating on 3D data directly, LocNet [2]} converts point cloud scans to 2D rotational invariant representation for searching similar frames in the global prior map, and then performs the iterative closest point (ICP) methods to calculate global pose. Towards proposing a learning based LIDAR localization framework that directly processes point clouds, L3-Net [3]} processes point cloud data with PointNet [4]} to extract feature descriptors that encode certain useful properties, and models the temporal connections of motion dynamics via a recurrent neural network. It optimizes the loss between the predicted poses and ground truth values by minimizing the matching distance between the point cloud input and the 3D map. Some techniques, such as PointNetVLAD [5]}, PCAN [6]} and D3Feat [7]} explored to retrieve the reference scene at the beginning, while other techniques such as DeepICP [8]} and DCP [9]} allow to estimate relative motion transformations from 3D scans. Compared with image-based relocalization including 2D-to-3D and 2D-to-2D localization, 3D-to-3D localization is relatively underexplored.
